Parametric modeling and optimization of the intake and exhaust phases of a hydrogen Wankel rotary engine using parallel computing optimization platform - Wang, Huaiyu; Ji, Changwei; Shi, Cheng; Yang, Jinxin; Ge, Yunshan; Wang, Shuofeng; Chang, Ke; Meng, Hao; Wang, Xin (Fuel (Guildford), 2022)
[Display omitted] •A parametric model of intake and exhaust phases of Wankel rotary engine is developed.•Optimized intake and exhaust phases increase the indicated mean effective pressure.•Multi-objective particle swarm optimization algorithm is enhanced by Sobol sequence.•Sensitivity of performance and emissions to intake and exhaust phases is evaluated.•A parallel computing optimization platform based on MOPSO is established. Focusing on performance and emissions optimization, a novel parallel computing optimization platform was implemented to optimize the intake and exhaust phases of a hydrogen Wankel rotary engine (WRE). An improved multi-objective particle swarm algorithm implemented with the Sobol sequence was introduced in this study, which makes it superior in global search. A one-dimensional model integrating the leakage models was built and validated under various excess air ratios. The parametric control variables of the intake and exhaust phases were defined as rise stage, main stage, and decline stage. The indicated mean effective pressure (IMEP), indicated specific fuel consumption (ISFC), and nitrogen oxide (NOx) were used as evaluation objectives. The optimization results showed that there was a quadratic relationship between ISFC and IMEP, and the ISFC decreased with increasing IMEP. The relationship between NOx and IMEP was closer to linear, and the NOx increased with the increase of IMEP. The timing of intake port full closing (IPFC) contributed the most influence to IMEP and NOx, and a delayed IPFC resulted in a lower IMEP. The timing of exhaust port start opening (EPSO) significantly affected the ISFC, and an earlier EPSO resulted in a higher ISFC. In the optimal case, the IMEP was increased by 2.0%, ISFC was reduced by 1.1%, and NOx was only increased by 0.1%. It is a prospective approach to further improve performance and emissions simultaneously using parallel computing optimization platform.